1. Julia
If Fortran were redesigned from scratch for the 21st century, it would probably look a lot like Julia. Created specifically for high-performance numerical and scientific computing, Julia combines the performance of a compiled language like Fortran with
the user-friendly, dynamic feel of Python. Its syntax is clean and heavily influenced by mathematical notation, making it feel immediately familiar to anyone who appreciates Fortran's "Formula Translator" roots. The learning curve is gentle for Fortran veterans, with features like 1-based array indexing and a strong emphasis on array operations. Unlike Fortran, which can be awkward for general-purpose tasks, Julia is a more complete language that handles everything from data manipulation to web servers gracefully. With its just-in-time (JIT) compilation, it achieves impressive speeds that are competitive with, and sometimes exceed, optimized Fortran code. For new projects where you want Fortran-like speed without the legacy constraints, Julia is the obvious first choice.
2. Modern C++
For decades, C++ has been the other dominant force in high-performance computing. While older versions could be syntactically dense for numerical work, modern C++ (C++17, C++20, and beyond) has become much friendlier to the scientific programmer. If you appreciate Fortran for its proximity to the hardware and its raw speed, you'll find a similar philosophy in C++. The key difference is that C++ gives you a much richer set of abstractions. Libraries like Eigen, Blitz++, and the facilities in the standard library itself make complex linear algebra and numerical operations feel more natural than ever. Where Fortran has excellent native multi-dimensional array support, C++ now offers powerful constructs like `std::mdspan`. For Fortran developers who need to interface with other systems or who crave more control over memory and program structure without sacrificing performance, diving into modern C++ is a logical and powerful next step.
3. Python (with the Scientific Stack)
This might seem like an odd choice, given Python is an interpreted language known for being slower than compiled languages. However, the appeal here isn't Python itself, but its ecosystem. For the Fortran developer who values quick prototyping and access to a vast array of tools, Python is unmatched. The core of scientific Python—NumPy, SciPy, and pandas—is largely written in C and Fortran. This means you get the best of both worlds: a simple, readable front-end for scripting and exploration, with battle-tested, high-performance libraries doing the heavy lifting underneath. While you wouldn't write a large-scale fluid dynamics simulation from scratch in pure Python, you would absolutely use it to steer that simulation, analyze its output, and visualize the results. For Fortran programmers who need a powerful and flexible 'glue' language to complement their number-crunching code, Python is the industry standard.
4. Chapel
Here’s a name you might not know, but should. Developed by Cray, Chapel was designed from the ground up for productive, large-scale parallel computing. If you love Fortran for its coarrays and its ability to scale across supercomputers, Chapel takes those concepts and makes them central to the language. It offers high-level abstractions for both data and task parallelism, which can make writing complex distributed-memory programs feel almost as simple as writing for a single machine. The syntax is modern and clean, aiming to be as readable as Python while delivering performance comparable to Fortran or C. It’s a language built on a 'multiresolution philosophy,' allowing you to start with abstract parallel code and incrementally add lower-level details for fine-tuned performance. For developers focused on the future of HPC, Chapel is a fascinating glimpse of what comes next.
5. Rust
Rust is for the Fortran developer who fundamentally values correctness, control, and performance above all else. While not specifically a numerical language, Rust offers a compelling proposition: the speed of C/C++ with guaranteed memory safety, enforced at compile time. This focus on eliminating entire classes of bugs (like null pointer dereferences and data races) will resonate with anyone who appreciates Fortran's restrictive nature, which historically made it hard to write slow or incorrect code. The scientific computing ecosystem in Rust is still growing, with libraries like `ndarray` providing powerful array capabilities. But its real appeal is for building robust, high-performance libraries that can then be called from other languages like Python. If your work involves creating foundational tools that need to be both blazingly fast and utterly reliable, Rust shares a deep philosophical connection with the very reasons Fortran has endured for so long.
